Decision Support Based on Time-Series Analytics: A Cluster Methodology

  • Wanli Xing
  • Rui Guo
  • Nathan Lowrance
  • Thomas Kochtanek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8522)


Web analytic techniques have become increasingly popular, particularly Google Analytics time-series dashboards. But interpretations of a website’s visits traffic data may be oversimplified and limited by Google Analytics existing functionalities. This means website mangers have to make estimations rather than mathematically informed decisions. In order to gain a more precise view of longitudinal website visits traffic data, the researchers mathematically transformed the existing Goggle Analytics’ log data allowing the vectors of website visits per each year to be considered simultaneously. The methodology groups the data of an example website gathered over an ‘x’ year period into ‘y’ clusters of data. The results show that the transformed data is richer, more accurate and informative, potentially allowing website managers to make more informed decisions concerning promoting, developing, and maintaining their websites rather than relying on estimations.


Temporal analytics Google analytics cluster analysis decision support website management 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Marek, K.: Getting to Know Web Analytics. Using web analytics in the library, pp. 11–16. ALA Store (2011)Google Scholar
  2. 2.
    Clifton, B.: Advanced web metrics with Google Analytics. John Wiley and Sons, Inc., Indianapolis (2012)Google Scholar
  3. 3.
    Phippen, A., Sheppard, L., Furnell, S.: A practical evaluation of Web analytics. Internet Research 14(4), 284–293 (2004)CrossRefGoogle Scholar
  4. 4.
    Mullarkey, G.W.: Internet measurement data - practical and technical issues. Marketing Intelligence & Planning 22(1), 42–58 (2004)CrossRefGoogle Scholar
  5. 5.
    Dreze, X., Zufryden, F.: Is Internet Advertising Ready for Prime Time? Journal of Advertising Research 38(3), 7–18 (1998)Google Scholar
  6. 6.
    Grimes, C., Tang, D., Russell, D.: Query Logs Are Not Enough. In: Workshop on Query Logs Analysis: Social and Technological Challenges, Banff, Canada (2007)Google Scholar
  7. 7.
    Weischedel, B., Huizingh, E.: Web Site Optimization With Web Metrics: A Case Study. In: International Conference on Electronic Commerce (ICEC 2006), pp. 463–470 (2006)Google Scholar
  8. 8.
    Khoo, M., Pagano, J., Washington, A.L., Recker, M., Palmer, B., Donahue, R.A.: Using web metrics to analyze digital libraries. In: Proceedings of the 8th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 375–384. ACM (2008)Google Scholar
  9. 9.
    Jansen, B.J., Spink, A.: How are we searching the World Wide Web? A comparison of nine search engine transaction logs. Information Processing & Management 42(1), 248–263 (2005)CrossRefGoogle Scholar
  10. 10.
    Beitzel, S.M., Jensen, E.C., Chowdhury, A., Frieder, O., Grossman, D.: Temporalanalysis of a very large topically categorized web query log. Journal of the American Society for Information Science and Technology 58(2), 166–178 (2007)CrossRefGoogle Scholar
  11. 11.
    Zhang, Y., Jansen, B.J., Spink, A.: Time series analysis of a Web search engine transaction log. Information Processing & Management 45(2), 230–245 (2009)CrossRefGoogle Scholar
  12. 12.
    Chi, Y., Zhu, S., Song, X., Tatemura, J., Tseng, B.L.: Structural and temporal analysisof the blogosphere through community factorization. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2007)Google Scholar
  13. 13.
    Farney, T., Mchale, N.: Data Viewing and Sharing: Utilizing Your Data to the Fullest. Library Technology Reports 49(4), 39–42 (2013)Google Scholar
  14. 14.
    Kirk, M., Morgan, R., Tonkin, E., McDonald, K., Skirton, H.: An objective approach to evaluating an internet-delivered genetics education resource developed for nurses: using Google AnalyticsTM to monitor global visitor engagement. Journal of Research in Nursing 17(6), 557–579 (2012)CrossRefGoogle Scholar
  15. 15.
    Pakkala, H., Presser, K., Christensen, T.: Using Google Analytics to measure visitor statistics: The case of food composition websites. International Journal of Information Management 32(6), 504–512 (2012)CrossRefGoogle Scholar
  16. 16.
    Kent, M.L., Carr, B.J., Husted, R.A., Pop, R.A.: Learning web analytics: A tool for strategic communication. Public Relations Review 37(5), 536–543 (2011)CrossRefGoogle Scholar
  17. 17.
    Kumar, C., Norris, J.B., Sun, Y.: Location and time do matter: A long tail study of website requests. Decision Support Systems 47(4), 500–507 (2009)CrossRefGoogle Scholar
  18. 18.
    Plaza, B.: Monitoring web traffic source effectiveness with Google Analytics: An experiment with time series. Aslib Proceedings 61(5), 474–482 (2009)CrossRefGoogle Scholar
  19. 19.
    Plaza, B.: Google Analytics for measuring website performance. Tourism Management 32(3), 477–481 (2011)CrossRefMathSciNetGoogle Scholar
  20. 20.
    Wang, X., Shen, D., Chen, H.L., Wedman, L.: Applying web analytics in a K-12 resource inventory. The Electronic Library 29(1), 20–35 (2011)CrossRefzbMATHGoogle Scholar
  21. 21.
    Guo, R., Zhang, Y.: Identifying Time-of-Day Breakpoints Based on Nonintrusive Data Collection Platforms. Journal of Intelligent Transportation Systems (2013)Google Scholar
  22. 22.
    Everitt, B.S., Landau, S., Leese, M.: Cluster Analysis, 5th edn. John Wiley & Sons, Ltd. (2011)Google Scholar
  23. 23.
    Tibshirani, R., Walther, G., Hastie, T.: Estimating the Number of Clusters in a Data Set via the Gap Statistic. Journal of Royal Statistical Society, B63, Part 2, 411–423 (2001)Google Scholar
  24. 24.
    Rousseeuw, P.J.: Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis. Journal of Computational and Applied Mathematic 20(1), 53–65 (1987)CrossRefzbMATHGoogle Scholar
  25. 25.
    Xu, C., Liu, P., Wang, W., Li, Z.: Evaluation of the impacts of traffic states on crash risks on freeways. Accident Analysis & Prevention 47, 162–171 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Wanli Xing
    • 1
  • Rui Guo
    • 2
  • Nathan Lowrance
    • 1
  • Thomas Kochtanek
    • 1
  1. 1.School of Information Science and Learning TechnologiesUniversity of MissouriColumbiaUSA
  2. 2.Department of Civil and Environmental EngineeringUniversity of South FloridaTampa, ColumbiaUSA

Personalised recommendations